README.md

metadata

license: apache-2.0
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - cats_vs_dogs
metrics:
  - accuracy
base_model: google/vit-base-patch16-224-in21k
model-index:
  - name: vit-base-cats-vs-dogs
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: cats_vs_dogs
          type: cats_vs_dogs
          args: default
        metrics:
          - type: accuracy
            value: 0.9883257403189066
            name: Accuracy

vit-base-cats-vs-dogs

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cats_vs_dogs dataset. It achieves the following results on the evaluation set:

• Loss: 0.0369
• Accuracy: 0.9883

how to use

from transformers import ViTFeatureExtractor, ViTModel
from PIL import Image
import requests

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
model = ViTModel.from_pretrained('akahana/vit-base-cats-vs-dogs')
inputs = feature_extractor(images=image, return_tensors="pt")

outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 8
• eval_batch_size: 8
• seed: 1337
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0949	1.0	2488	0.0369	0.9883

Framework versions

• Transformers 4.12.5
• Pytorch 1.10.0+cu111
• Datasets 1.16.1
• Tokenizers 0.10.3